iris_prtl: A `randomForestSRC::plot.variable` object.

Description

A cached object from randomForestSRC::plot.variable function for the iris randomForestSRC classification forest iris_rf.

Arguments

format

randomForestSRC::plot.variable object for classification

Details

For ggRandomForests examples and tests, as well as streamlining the R CMD CHECK for package release, we cache the computationally expensive operations from the randomForestSRC package.

We build a regression randomForest (iris_rf) with the iris data, then run the plot.variable function to generate the data for constructing partial dependence plots.

This "data set" is a cache of the randomForestSRC::plot.variable function, with partial=TRUE for the "Petal.Width" variable. The data is then a risk adjusted variable dependence curve from the iris_rf random forest model.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (has iris3 as iris.)

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188.?

Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2-5.

Ishwaran H. and Kogalur U.B. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.4.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25-31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841-860.

Examples

Run this code

## veteran data
## randomized trial of two treatment regimens for lung cancer
data(iris_rf, package = "ggRandomForests")
iris_prtl <- plot.variable(iris_rf, xvar.names = "Petal.Width",
                             partial=TRUE)

plot(iris_prtl)